INsIDER: Imaging the Interplay Between Axonal Damage and Repair in Multiple Sclerosis
Trial Snapshot
- Phase
- Not Applicable
- Status
- Recruiting
- Sponsor
- Enrollment
- 300
- Locations
- 1
- Primary Endpoint
- MRI- change in axonal integrity and organization over 2 years in aMS, naPMS and HC, by using machine learning techniques
Study Overview
Brief Summary
This project is to:
- Quantify differences in axonal integrity and organization in aMS versus naPMS patients.
- Quantify changes in axonal integrity and organization in aMS versus naPMS patients over a two-year period.
- Validate the combination of imaging parameters that best differentiate aMS versus naPMS patients using histopathology.
Detailed Description
Multiple sclerosis (MS) is a chronic autoimmune disease of the central nervous system characterized by multifocal inflammatory infiltrates, microglial activation and degradation of oligodendrocytes, myelin and axons. Clinical MS categories exhibit variable amount of central nervous system (CNS) damage and repair, depending on numerous variables including genetic, immunological, pathological and environmental factors.
Therefore, understanding the interplay between axonal damage (i.e. axonal demyelination/degeneration/loss/disorganization) and (ii) axonal repair (i.e. axonal remyelination/reorganization) in living MS patients may be the key to understand disease progression, to establish accurate disease monitoring criteria and to predict disease response to future reparative therapies. New in-vivo methods are necessary to elucidate the interplay between axonal damage and repair in the brain of living patients with MS. Advanced MRI (aMRI) permits a multifaced quantification of the various components of the axons and their organization. Neurite Orientation Dispersion and Density Imaging (NODDI) and Diffusion Kurtosis (DK) are new approaches in clinical research This study is to identify in vivo the specific neuropathological pattern of axonal damage and repair exhibited by active MS (aMS) and non-active progressive MS (naPMS) patient by leveraging the information provided by model-based diffusion metrics (NODDI, DK), Magnetization Transfer Imaging (MTI), Multi-echo Susceptibility-Based imaging (SBI), Myelin Water Imaging (MWI) and quantitative T1 relaxometry (qT1). These advanced MRI contrasts provide complementary and partially redundant information about the axonal structure and its organization (i.e. density and orientation of axons and dendrites in the brain tissue, axonal integrity and myelination, presence of myelin and iron, and brain tissue architecture). Therefore, their combination may prove high sensitivity and specificity to axonal damage and repair.
This project has 3 main aims:
Aim 1. Quantify differences in axonal integrity and organization in aMS versus naPMS patients.
Aim 2. Quantify changes in axonal integrity and organization in aMS versus naPMS patients over a two-year period.
Study Design
- Study Type
- Observational
- Observational Model
- Cohort
- Time Perspective
- Prospective
Eligibility Criteria
- Ages
- 18 Years to 80 Years (Adult, Older Adult)
- Sex
- All
- Accepts Healthy Volunteers
- Yes
Inclusion Criteria
- Not provided
Exclusion Criteria
- Not provided
Outcomes
Primary Outcomes
MRI- change in axonal integrity and organization over 2 years in aMS, naPMS and HC, by using machine learning techniques
Time Frame: at baseline and 2 years (+/- 3 months) after baseline
After magnetic resonance (MR) data preprocessing (image denoising, standardization, bias field correction) classical machine learning techniques will be used to classify a number of MRI metrics which will be averaged over a number of regions of interest (ROIs) including (i) normal-appearing white and brain matter in brain lobes and cervical spinal cord, (ii) basal ganglia, (iii) thalamus, (vi) cerebellum, MS lesions. Complex input data (voxels/patches) will be generated to learn from, then a deep learning model for supervised classification will be defined to identify the combination of aMRI parameters that characterize aMS, naPMS and HC.
Secondary Outcomes
- Change in Hospital Anxiety and Depression Scale (HADS)(at baseline and 2 years (+/- 3 months) after baseline)
- Change in Symbol Digital Modalities Test (SDMT)(at baseline and 2 years (+/- 3 months) after baseline)
- Change in auditory verbal learning and memory test/ Verbaler Lern- und Merkfähigkeitstest (VLMT)(at baseline and 2 years (+/- 3 months) after baseline)
- Change in Brief Visuospatial Memory Test (BVMT)(at baseline and 2 years (+/- 3 months) after baseline)
- Change in MUSIC Test(at baseline and 2 years (+/- 3 months) after baseline)